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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Deriving Transit Performance Metrics from GTFS Data</title>
      <link>https://rip.trb.org/View/2732356</link>
      <description><![CDATA[Transit agencies devote extensive resources to producing General Transit Feed Specification (GTFS) Schedule and Realtime data to power trip planning applications. In representing scheduled and actual service characteristics, these data offer a theoretical off-label use to generate metrics of transit service performance. This project seeks to create and test a set of standardized protocols for deriving and visualizing these performance metrics from raw GTFS feeds. These protocols would be established in such a way as to enable transit agencies, planning organizations, transportation researchers, transit advocates, and community-based organizations to easily implement them on any transit system with available GTFS schedule and GTFS realtime feeds. Specific metrics would look at common transit issues tied to schedule deviation – from on-time performance to bus bunching – but at a more granular spatial and temporal level than ever before possible. This detail, literally at the stop and segment level, is designed to enable more effective transit planning and advocacy.

The research would first collect a multi-day sample of GTFS schedule and realtime data from one or more transit agencies. This information would serve as the core data for the entire project. These data would be stored in a relational database that enables spatial analysis, such as PostGIS. The research would first design appropriate cleaning and aggregating protocols to prepare the data for performance analysis. A web-based tool, likely using D3, would be designed to allow a user to interact with the data to generate and visualize the transit performance metrics. The tool would allow fine-grained filtering by location and time period to enable detailed analysis of transit performance. A key feature of this approach is to allow interactivity with the data.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:26:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732356</guid>
    </item>
    <item>
      <title>Statewide Multimodal Destination Access Methods and Demographic Analysis</title>
      <link>https://rip.trb.org/View/2725639</link>
      <description><![CDATA[The Oregon Department of Transportation (ODOT) does not currently have a consistent, statewide method to evaluate destination access—whether people can reliably and affordably reach essential destinations such as employment, education, health care, and key services. While agency performance measures and analyses focus primarily on infrastructure conditions and system mobility, they do not answer whether investments are improving people’s ability to access what they need for daily life. Without a standardized destination access methodology, ODOT lacks a clear, data-driven basis for monitoring progress, understanding structural access gaps, or using access outcomes to inform investment decisions. 
OBJECTIVES: (1) Establish a standardized, agency-wide methodology for multimodal destination access analysis. ODOT currently performs destination access analysis on an ad hoc basis and does not have a consistent, documented method for statewide or cross-program use. This project will develop and test a unified approach that can be used across business lines for performance reporting, planning, and investment decision-making. (2) Integrate user profile analysis to identify which populations face transportation access gaps—and to what extent. This research will move beyond single-variable demographic assumptions and instead use data-informed definitions of at-risk populations to better understand who experiences structural access barriers and why. (3) Develop a tool for viewing destination accessibility metrics. The tool can be used to view accessibility by mode and destination type by region. The combination of transportation and land use data will enable planners to understand existing accessibility conditions and needs in specific areas.  This tool would be usable for the ODOT Capital Investment Plan (CIP), local transportation system plans, and other programs where access measures offer utility. 
This research fulfills a need for a destination access methodology that supports ODOT policy, planning, and prioritization. With the results of this research, ODOT will be able to answer critical questions about how the transportation system is serving residents. Access metrics can play a critical role in vehicle miles of travel (VMT) per capita and emissions reduction strategies by informing staff on which areas have feasible multimodal access. ]]></description>
      <pubDate>Wed, 08 Jul 2026 16:48:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725639</guid>
    </item>
    <item>
      <title>Assessing the Reliability of Hard Braking and Other Vehicle-Based Metrics as Surrogate Safety Measures</title>
      <link>https://rip.trb.org/View/2712175</link>
      <description><![CDATA[Transportation safety is a critical concern for agencies nationwide, and recent technological advances have enabled the collection of vast amounts of vehicle trajectory and behavioral data. Among these data, hard-braking events have emerged as a surrogate safety measure, which can be used to infer the possibility of near misses and crashes. Agencies and data providers increasingly rely on metrics such as hard braking, excessive acceleration, and high-speed cornering, derived from connected vehicle (CV) data, to identify risky driving behavior and to proactively address safety risks.

Unlike traditional crash data, which are retrospective and often delayed, surrogate safety measures offer real-time insights into roadway conditions and driver behavior. This immediacy allows for earlier identification of emerging safety concerns and the implementation of timely countermeasures. However, the reliability and validity of these surrogate measures are not universally established. Their effectiveness can vary depending on factors such as sight distance, geometric design, speed limits, traffic control devices, work zone configurations, and queue warning locations. For instance, a hard-braking event at a congested urban intersection may indicate different risks than one on a rural freeway.

The objective of this research is to rigorously evaluate the reliability and validity of hard braking and other vehicle-based metrics, such as excessive acceleration and high-speed cornering, as surrogate safety measures across diverse transportation environments. ]]></description>
      <pubDate>Tue, 09 Jun 2026 13:01:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712175</guid>
    </item>
    <item>
      <title>Research for the AASHTO Standing Committee on Planning. Task 74. Customer Research Practices and Applications in Transportation</title>
      <link>https://rip.trb.org/View/2706278</link>
      <description><![CDATA[The objective of this research is to systematize knowledge on transportation customer research. It is to compile and disseminate practice, including but not limited to these elements: (1) Questions, formats and alternative methods for obtaining customer ratings of satisfaction and importance of transportation services and products. Are there standard questions or metrics that agencies could consider that would facilitate benchmarking across agencies for those who are interested? (2) Approaches to designing and directing customer research projects to obtain valid and reliable results. (3) Approaches to applying customer research to such decisions as prioritizing investments, allocating budgets, or redesigning services and products.]]></description>
      <pubDate>Wed, 27 May 2026 14:49:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706278</guid>
    </item>
    <item>
      <title>Phase II: After Study Evaluation of Interstate 4 (I-4) Florida's Regional Advanced Mobility Elements (FRAME) Project (After Analysis)</title>
      <link>https://rip.trb.org/View/2706007</link>
      <description><![CDATA[Restart of BED26-977-08. The objective of this research project is to develop the evaluation plan for the after conditions of the I-4 FRAME project. Then, before/after study for the evaluation metrics will be conducted to identify the degree of improvement (or not) for every metric from the before to the after observations. The study findings will be analyzed and documented. To conduct the task, the research team will perform the following activities: (1)  determine the evaluation criteria tailored to the I-4 FRAME project objectives; (2) describe the data collection procedures tailored to these criteria that are needed to report on the achievement of project objectives; and (3) document how the I-4 FRAME project addressed the safety challenges on the project corridors compared with Phase ? (before).]]></description>
      <pubDate>Fri, 22 May 2026 09:10:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706007</guid>
    </item>
    <item>
      <title>A Framework for Integrated Quality of Service Evaluation using Operational and Safety Considerations</title>
      <link>https://rip.trb.org/View/2703926</link>
      <description><![CDATA[This project addresses a critical gap in transportation decision-making by examining the relationships among safety and operational performance measures that State DOTs typically use for planning, design, and operations. While agencies rely on different metrics depending on application, such as crash-based measures for safety projects and travel time reliability or delay for congestion management, there is limited guidance on how these measures interact or how they should be jointly considered when evaluating alternatives. Using multi-source data from State DOTs and third-party providers along major corridors in Region VII, the project will quantify correlations, trade-offs, and synergies among key performance measures and develop a practical, multi-objective evaluation framework tailored to common DOT applications. The resulting framework and guidance will enable agencies to conduct more consistent, transparent, and context-sensitive evaluations that better balance safety and operational objectives.
]]></description>
      <pubDate>Thu, 21 May 2026 22:41:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703926</guid>
    </item>
    <item>
      <title>Successful Approaches to Applying Project Management Performance Metrics to Achieve Strategic Process Improvements</title>
      <link>https://rip.trb.org/View/2681236</link>
      <description><![CDATA[Transportation agencies use a range of project management performance measures to track the status of engineering and project delivery activities. Most state departments of transportation (DOTs) monitor “on-time” and “on-budget” performance and report these measures to internal leadership and external stakeholders. While these metrics appear straightforward, definitions and calculation methods vary significantly across agencies, influencing how performance is interpreted and communicated.
Given public expectations for timely and cost-effective project delivery, DOTs are seeking practical approaches to more consistently track development progress and clearly communicate results to stakeholders.
OBJECTIVE: This scan will document how state DOTs define, measure, and apply “on-time” and “on-budget” performance metrics. The team will examine key elements such as:
When measured activities begin and end; How and when current schedules are compared to baseline schedules; Which cost estimates are used to establish baselines and track current performance, and at what project milestones.

Recognizing the interrelationship among scope, schedule, and budget, the scan will also explore how agencies monitor and manage scope throughout project development, if and how scope changes are captured, and how those changes inform performance reporting.
In addition, the scan will document the organizational structures supporting project delivery performance management, including centralized and decentralized models (e.g., project management offices, Chief Engineer’s Offices, strategic initiatives offices). The study will identify practices that support effective implementation and reporting of established performance measures.
]]></description>
      <pubDate>Tue, 17 Mar 2026 15:06:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681236</guid>
    </item>
    <item>
      <title>Developing Simplified and Unified Planning-Level Metrics for Operational Benefits </title>
      <link>https://rip.trb.org/View/2663585</link>
      <description><![CDATA[Transportation agencies increasingly rely on Performance-Based Planning and Programming (PBPP) to make transparent, data-driven investment decisions and to evaluate and prioritize projects. In Virginia, Virginia Department of Transportation (VDOT) and the Office of Intermodal Planning and Investment have advanced PBPP through VTrans, Project Pipeline, and SMART SCALE, a recognized program for ranking investments across safety, congestion, accessibility, environmental quality, and economic development. In addition, VDOT’s Project Planning function establishes a long-term vision for the transportation system. Despite progress in Virginia’s planning framework, no rapid and transferable methodology exists to quantify operational benefits of roadway and ITS improvements. Current approaches, such as the Interstate Operations and Enhancement Program and SMART SCALE , provide valuable insights but lack standardized lightweight measures of operational benefit metrics. When VDOT needs to compare alternatives or screen early-stage concepts, analysts must rely on microsimulation, HCM procedures, or project-specific before–after studies. These methods are accurate but slow, data-intensive, and not scalable to dozens or hundreds of candidate projects. The absence of standardized, easy-to-apply operational metrics constrains Virginia’s ability to efficiently screen projects, communicate benefits, and ensure consistent decision-making within PBPP. This research will define Operational Modification Factors, a proportional before/after change derived from operational benefit metrics, and develop a reusable and transferable planning-level methodology that enables VDOT to estimate operational benefits rapidly using limited inputs (e.g., v/c ratio, facility type, AADT, geometry, improvement type). 
]]></description>
      <pubDate>Tue, 03 Feb 2026 10:42:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663585</guid>
    </item>
    <item>
      <title>Resilience Performance Measures</title>
      <link>https://rip.trb.org/View/2593942</link>
      <description><![CDATA[Each year Kentucky spends millions of dollars repairing roads and bridges damaged by extreme weather and natural hazards. One way to bring down long-term maintenance costs and reduce system disruptions is to improve infrastructure resiliency. Developing performance measures that quantify the effectiveness of resilience improvements is critical for verifying resilience-oriented goals align with broader policy objectives, that resources are utilized efficiently, and that future investments yield the greatest possible returns. By adopting standardized resilience metrics, Kentucky Transportation Cabinet (KYTC) can strengthen the reliability and durability of transportation infrastructure and better serve communities in the face of disruptions caused by extreme weather and other hazards.]]></description>
      <pubDate>Thu, 28 Aug 2025 11:32:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593942</guid>
    </item>
    <item>
      <title>Pavement Distress Evaluation and Cracking Indices Generation using Deep Learning</title>
      <link>https://rip.trb.org/View/2563770</link>
      <description><![CDATA[The North Carolina Department of Transportation (NCDOT) manages the nation's second-largest roadway network. To ensure safety and efficiency of this network, it is crucial to implement timely and effective maintenance strategies. This research project aims to address these needs. 

In this research, to help optimize maintenance strategies, non-crack distresses will be classified, segmented, and quantified using cutting-edge deep learning techniques. Since an on-going research project has already completed similar tasks for varying types of cracks, upon completion of this proposed study, all types (crack and non-crack) of distresses across the 14 Divisions monitored by NCDOT can be classified and quantified using deep learning models. With this approach, it is estimated that a comprehensive state-wide pavement performance assessment can be completed in one week. Consequently, the outcomes of this proposed study, combined with those from the on-going research, will enable timely updates of distress indices and Pavement Condition Rating (PCR) values. This enhanced responsiveness of NCDOT’s PMS will significantly benefit North Carolina’s roadway network in terms of durability and sustainability. In addition, specific crack metric and index, namely the Pavement Surface Cracking Metric (PSCM) and the Pavement Surface Cracking Index (PSCI), will be calculated using the ASTM E3303-21 standard. The calculated results will be highly accurate, as the length of every crack is quantified at a pixel level. Moreover, this task will standardize and enhance the reliability of crack assessments, contributing to a more effective PMS managed by NCDOT.

One potential challenge that the UNC Charlotte researchers face is identifying certain types of uncommon non-crack distresses from the raw images provided by NCDOT. The lack of training data for these distresses can directly impact the performance of the corresponding deep learning models. To address this issue, the researchers plan to work closely with NCDOT engineers to pinpoint the locations of these distresses and gather sufficient distress data for model training purposes. Another potential challenge is the time-consuming nature of the image annotation process, a common obstacle in studies utilizing deep learning techniques for image processing. Building on the experience gained from the on-going study, the researchers plan to evaluate both AI-based and self-supervised learning approaches to expedite the annotation process effectively. 

It should be noted that transferred learning from deep learning models developed in the on-going NCDOT research project will be used to develop new models in this study. This approach allows resources spent on one task to be transferred, reused, and adapted for other related tasks, significantly reducing the computational resources and time, and more importantly, leading to improved performance of newly developed models.

In summary, this research project is proposed to improve maintenance efficiency, reduce repair costs, and support NCDOT’s sustainability goals. Various approaches will be utilized to ensure the success of this project. The methods and tools developed in this project can be applied to address other challenges in the future.
]]></description>
      <pubDate>Fri, 13 Jun 2025 12:48:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2563770</guid>
    </item>
    <item>
      <title>Guide to Measuring Community Mobility Resilience

</title>
      <link>https://rip.trb.org/View/2558413</link>
      <description><![CDATA[Community mobility considers how residents of a defined geographic area access and use the transportation options available to them. Community mobility can be disrupted by external or human factors. External disruptions include natural risks such as floods or fires that obstruct the use of available transportation options. Human factors include rapid changes to transportation demand that affect community mobility performance that communities cannot readily mitigate. For example, the growth of e-commerce contributes to congestion on some residential streets and business corridors, often blocking sidewalks and transit stops due to curb management issues. Additionally, teleworking has contributed to local population growth in rural areas, redistributed trips, and shifted peak hours. Each of these has implications for the performance of the transportation systems serving affected communities. 

State departments of transportation (DOTs) seek to determine the resilience of a transportation network using performance measures to maintain community mobility in the face of disruption. Measures can enable proactive assessment, pinpoint problems, and enable state DOTs to quickly address them. However, risks to community mobility are varied and resilience measures must work within the context of the individual community. Therefore, a one-size-fits-all approach to measuring the resilience of community mobility is not appropriate. Research is needed to identify a process to select appropriate community mobility resilience performance metrics. 

OBJECTIVE: The objective of this research is to develop a guide with resources to support state DOTs through a process of defining performance metrics and methods to assess the mobility resilience of the multimodal transportation system serving a given community.]]></description>
      <pubDate>Tue, 27 May 2025 20:36:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558413</guid>
    </item>
    <item>
      <title>Disabled Parking CV: Scalable Methods to Analyze Disability Parking using Computer Vision and High-Resolution Aerial and Streetscape Images</title>
      <link>https://rip.trb.org/View/2553158</link>
      <description><![CDATA[People with disabilities disproportionately rely on public transportation to access employment, education, and healthcare services; however, public transit is not always available or equally distributed, which excludes social and community participation. Car transit is thus the only viable alternative. Since the Americans with Disability Act (ADA) of 1990, 4-8% of public parking spaces need to be reserved for drivers/passengers with disabilities, providing wide, accessible spaces close to destinations. And yet, there has been no systematic, large-scale study of the allocation and sizes of disability parking spaces across the US. The limited prior work that does exist has employed questionnaire methods to survey disabled drivers or examines the appropriate design of the disabled parking spot itself (e.g., its dimensions).

In this project, the research team proposes building and evaluating state-of-the-art computer vision (CV) methods applied to emerging high resolution aerial photography—such as the open 0.08 meter/pixel orthoimagery of Washington DC (DC Orthophoto, 2021)—to semi-automatically (1) track the allocation of disability parking in public and commercial lots; (2) examine characteristics of said parking (e.g., size, access area, % of allotment to normal parking) as well as public transportation ridership usage; (3) and create new analytic metrics enabled by this approach such as such as the proximity of disabled spaces to POIs (e.g., the distance to an entrance). 

The overarching goal of this work is to create open datasets and analytics for ADA-accessible parking as well as to infuse this information into modern mapping tools (e.g., OpenStreetMaps).
]]></description>
      <pubDate>Tue, 13 May 2025 19:30:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553158</guid>
    </item>
    <item>
      <title>Development of a Scalable, Low-Cost, Environmentally-Friendly Adaptive Traffic Signal Control (SLE-ATSC) System</title>
      <link>https://rip.trb.org/View/2495005</link>
      <description><![CDATA[As an enhanced method for vehicle detection at signalized intersections, it is possible to use vehicle-probe data from smartphones, Global Navigation Satellite System (GNSS) receivers, and other types of mobile devices to complement existing traffic sensing and signal control, resulting in lower energy consumption. Using these additional data, it is now possible to estimate reliable traffic queue lengths at high-density traffic intersections. Given real-time reliable traffic queue lengths, it is possible then to dynamically adjust the signal phase and timing of an intersection, with the goal of minimizing traffic queues, waiting times, and energy use. Using UC Riverside’s Innovation Corridor as a target arterial roadway, the research team will develop a scalable, low-cost, environmentally-friendly adaptive traffic signal control (SLE-ATSC) system based on receiving real-time traffic data from sources such as TomTom and INRIX. The signal control system will be implemented for several of the key intersections along the corridor, using a calibrated state-of-the-art traffic simulation platform. Various metrics will be evaluated, comparing the existing traffic signal phase and timing to the new dynamic signal phase and timing resulting from the adaptive signal control system. Using the calibrated simulation model, traffic system metrics will be estimated. In addition, part of the research team (TSU) will utilize their driving simulators as part of a “Hardware-in- the-Loop” testing system for the proposed adaptive traffic signal control system. The traffic simulation model developed at UCR will interface directly with the TSU driving simulators, allowing the research team to see more realistic driving behavior operating in the simulation platform. This will provide more realistic measures of the overall system performance, with a focus on safety, mobility, and environmental metrics.]]></description>
      <pubDate>Fri, 31 Jan 2025 16:35:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495005</guid>
    </item>
    <item>
      <title>How Do Mode-Specific Network Metrics Impact Safety Outcomes?</title>
      <link>https://rip.trb.org/View/2472695</link>
      <description><![CDATA[Public transit and active transportation networks have been associated with improvements in multimodal traffic safety, yet their impacts in rural and peri-urban areas remain underexplored. This research evaluates the role of mode-specific network metrics—quantifying size, structure, and connectivity—on crash outcomes across New England using crash data and community characteristics. Predictive models incorporating regression and machine learning methods will identify which network features influence safety, providing actionable insights for planners and policymakers. Outputs include an open-access dataset of network metrics, a dashboard for visualizing results, and a decision-support tool for improving roadway safety across varied community contexts.
]]></description>
      <pubDate>Mon, 09 Dec 2024 10:04:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2472695</guid>
    </item>
    <item>
      <title>Improve Utility Investigations through AI Data Fusion and Reliable Quality Assessments</title>
      <link>https://rip.trb.org/View/2437689</link>
      <description><![CDATA[Identifying and documenting existing utility facilities within the proposed right-of-way (ROW) is crucial for successful project delivery. There is a need to leverage data collection technology’s strengths and minimize weaknesses for a more robust and reliable determination of utility locations and develop and test metrics to assess the utility investigation quality levels in ways that make sense to project design teams. Improvements will lead to a better stakeholder understanding to communicate the quality levels commonly used by the SUE industry (D, C, B, and A), the basis for assessing utility investigation deliverable quality, and would be able to tie quality levels to quantifiable performance metrics such as positional accuracy, error, and completeness, which are common in engineering and surveying when collecting field data for a project.]]></description>
      <pubDate>Thu, 03 Oct 2024 10:31:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2437689</guid>
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